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Bluetooth Low Energy (BLE) devices use public (non-encrypted) advertising channels to announce their presence to other devices. To prevent tracking on these public channels, devices may use a periodically changing, randomized address instead of their permanent Media Access Control (MAC) address. In this work we show that many state-of-the-art devices which are implementing such anonymization measures are vulnerable to passive tracking that extends well beyond their address randomization cycles. We show that it is possible to extract identifying tokens from the pay-load of advertising messages for tracking purposes. We present an address-carryover algorithm which exploits the asynchronous nature of payload and address changes to achieve tracking beyond the address randomization of a device. We furthermore identify an identity-exposing attack via a device accessory that allows permanent, non-continuous tracking, as well as an iOS side-channel which allows insights into user activity. Finally, we provide countermeasures against the presented algorithm and other privacy flaws in BLE advertising.
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Gerry Wan, Aaron Johnson, Ryan Wails, Sameer Wagh and Prateek Mittal
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Dan Bogdanov, Liina Kamm, Baldur Kubo, Reimo Rebane, Ville Sokk and Riivo Talviste
We describe the use of secure multi-party computation for performing a large-scale privacy-preserving statistical study on real government data. In 2015, statisticians from the Estonian Center of Applied Research (CentAR) conducted a big data study to look for correlations between working during university studies and failing to graduate in time. The study was conducted by linking the database of individual tax payments from the Estonian Tax and Customs Board and the database of higher education events from the Ministry of Education and Research. Data collection, preparation and analysis were conducted using the Share-mind secure multi-party computation system that provided end-to-end cryptographic protection to the analysis. Using ten million tax records and half a million education records in the analysis, this is the largest cryptographically private statistical study ever conducted on real data.
Aaron D. Jaggard, Aaron Johnson, Sarah Cortes, Paul Syverson and Joan Feigenbaum
Motivated by the effectiveness of correlation attacks against Tor, the censorship arms race, and observations of malicious relays in Tor, we propose that Tor users capture their trust in network elements using probability distributions over the sets of elements observed by network adversaries. We present a modular system that allows users to efficiently and conveniently create such distributions and use them to improve their security. To illustrate this system, we present two novel types of adversaries. First, we study a powerful, pervasive adversary that can compromise an unknown number of Autonomous System organizations, Internet Exchange Point organizations, and Tor relay families. Second, we initiate the study of how an adversary might use Mutual Legal Assistance Treaties (MLATs) to enact surveillance. As part of this, we identify submarine cables as a potential subject of trust and incorporate data about these into our MLAT analysis by using them as a proxy for adversary power. Finally, we present preliminary experimental results that show the potential for our trust framework to be used by Tor clients and services to improve security.
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